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Task Panner Validator

Task Planner and Validator Skill Guide

cerbug450 installations selon la source☆ 0 Étoiles du dépôt

Installation

Suivez les instructions du dépôt pour votre agent. Consultez les fichiers et dépendances avant l’installation.

SkillHub CLI

skillhub install cerbug45/task-panner-validator

Examinez la source et la compatibilité, puis suivez les étapes d’installation.

Donnez le lien à votre agent et demandez-lui d’examiner instructions et dépendances avant d’expliquer les étapes.

Voir la source ↗Guides

Instructions

Task Planner and Validator - Skill Guide

This skill provides a secure, step-by-step task management system for AI Agents.

Quick Installation

# Clone the repository
git clone https://github.com/cerbug45/task-planner-validator.git
cd task-planner-validator

# That's it! No dependencies needed - pure Python standard library

Verify Installation

# Run tests
python test_basic.py

# Run examples
python examples.py

Basic Usage

1. Import and Initialize
from task_planner import TaskPlanner

# Create planner
planner = TaskPlanner(auto_approve=False)
2. Define Your Executor
def my_executor(action: str, parameters: dict):
    """Your custom execution logic"""
    if action == "fetch_data":
        # Fetch data from API, database, etc.
        return {"data": [1, 2, 3]}
    elif action == "process_data":
        # Process the data
        return {"processed": True}
    else:
        return {"status": "completed"}
3. Create a Plan
steps = [
    {
        "description": "Fetch user data",
        "action": "fetch_data",
        "parameters": {"source": "database"},
        "expected_output": "List of users"
    },
    {
        "description": "Process users",
        "action": "process_data",
        "parameters": {"validation": True},
        "expected_output": "Processed data"
    }
]

plan = planner.create_plan(
    title="Data Processing Pipeline",
    description="Fetch and process user data",
    steps=steps
)
4. Validate and Execute
# Validate
is_valid, warnings = planner.validate_plan(plan)
if warnings:
    print("Warnings:", warnings)

# Approve
planner.approve_plan(plan, approved_by="admin")

# Execute
success, results = planner.execute_plan(plan, my_executor)

# Get summary
summary = planner.get_execution_summary(plan)
print(f"Progress: {summary['progress_percentage']}%")

Key Features

Safety Validation

Automatically detects dangerous operatio...

Informations sur la source

Source
github
Repository
openclaw/skills
Path
skills/cerbug45/task-panner-validator
Version
—
Commit
c0fee08eb8a8a92e33fc9c2575abfa04fc2a9c35
Source de classification
rules
Statut
active
Visibilité
public

Ces informations proviennent de l’analyse indexée et peuvent être anciennes. Vérifiez les fichiers actuels.

README

Task Planner and Validator

A secure, step-by-step task management system designed for AI Agents to safely plan, validate, and execute complex multi-step tasks.

License: MIT Python 3.8+

🎯 Features

  • ✅ Step-by-Step Planning: Break down complex tasks into manageable, ordered steps
  • 🔒 Safety Validation: Built-in security checks for dangerous operations
  • 🔄 Rollback Support: Checkpoint system for reverting failed operations
  • 📝 Plan Persistence: Save and load plans in JSON format
  • 🎨 Integrity Verification: SHA-256 checksums to prevent tampering
  • ⚡ Execution Control: Dry-run mode, auto-approve, and stop-on-error options
  • 📊 Progress Tracking: Real-time status updates and execution summaries
  • 🔍 Detailed Logging: Comprehensive logging for debugging and auditing

🚀 Quick Start

Installation
# Clone the repository
git clone https://github.com/cerbug45/task-planner-validator.git
cd task-planner-validator

# No additional dependencies required - uses only Python standard library!
Basic Usage
from task_planner import TaskPlanner

# Create a planner
planner = TaskPlanner(auto_approve=False)

# Define your steps
steps = [
    {
        "description": "Fetch user data from API",
        "action": "fetch_data",
        "parameters": {"endpoint": "/api/users", "method": "GET"},
        "expected_output": "List of user objects",
        "safety_check": True,
        "rollback_possible": True
    },
    {
        "description": "Process and validate data",
        "action": "process_data",
        "parameters": {"validation_rules": ["email", "age"]},
        "expected_output": "Validated user data",
        "safety_check": True,
        "rollback_possible": True
    }
]

# Create a plan
plan = planner.create_plan(...

Contenu extrait

SKILL.md
# Task Planner and Validator - Skill Guide

This skill provides a secure, step-by-step task management system for AI Agents.

## Quick Installation

```bash
# Clone the repository
git clone https://github.com/cerbug45/task-planner-validator.git
cd task-planner-validator

# That's it! No dependencies needed - pure Python standard library
```

## Verify Installation

```bash
# Run tests
python test_basic.py

# Run examples
python examples.py
```

## Basic Usage

### 1. Import and Initialize

```python
from task_planner import TaskPlanner

# Create planner
planner = TaskPlanner(auto_approve=False)
```

### 2. Define Your Executor

```python
def my_executor(action: str, parameters: dict):
    """Your custom execution logic"""
    if action == "fetch_data":
        # Fetch data from API, database, etc.
        return {"data": [1, 2, 3]}
    elif action == "process_data":
        # Process the data
        return {"processed": True}
    else:
        return {"status": "completed"}
```

### 3. Create a Plan

```python
steps = [
    {
        "description": "Fetch user data",
        "action": "fetch_data",
        "parameters": {"source": "database"},
        "expected_output": "List of users"
    },
    {
        "description": "Process users",
        "action": "process_data",
        "parameters": {"validation": True},
        "expected_output": "Processed data"
    }
]

plan = planner.create_plan(
    title="Data Processing Pipeline",
    description="Fetch and process user data",
    steps=steps
)
```

### 4. Validate and Execute

```python
# Validate
is_valid, warnings = planner.validate_plan(plan)
if warnings:
    print("Warnings:", warnings)

# Approve
planner.approve_plan(plan, approved_by="admin")

# Execute
success, results = planner.execute_plan(plan, my_executor)

# Get summary
summary = planner.get_execution_summary(plan)
print(f"Progress: {summary['progress_percentage']}%")
```

## Key Features

### Safety Validation

Automatically detects dangerous operatio...
README
# Task Planner and Validator

A secure, step-by-step task management system designed for AI Agents to safely plan, validate, and execute complex multi-step tasks.

[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![Python 3.8+](https://img.shields.io/badge/python-3.8+-blue.svg)](https://www.python.org/downloads/)

## 🎯 Features

- **✅ Step-by-Step Planning**: Break down complex tasks into manageable, ordered steps
- **🔒 Safety Validation**: Built-in security checks for dangerous operations
- **🔄 Rollback Support**: Checkpoint system for reverting failed operations
- **📝 Plan Persistence**: Save and load plans in JSON format
- **🎨 Integrity Verification**: SHA-256 checksums to prevent tampering
- **⚡ Execution Control**: Dry-run mode, auto-approve, and stop-on-error options
- **📊 Progress Tracking**: Real-time status updates and execution summaries
- **🔍 Detailed Logging**: Comprehensive logging for debugging and auditing

## 🚀 Quick Start

### Installation

```bash
# Clone the repository
git clone https://github.com/cerbug45/task-planner-validator.git
cd task-planner-validator

# No additional dependencies required - uses only Python standard library!
```

### Basic Usage

```python
from task_planner import TaskPlanner

# Create a planner
planner = TaskPlanner(auto_approve=False)

# Define your steps
steps = [
    {
        "description": "Fetch user data from API",
        "action": "fetch_data",
        "parameters": {"endpoint": "/api/users", "method": "GET"},
        "expected_output": "List of user objects",
        "safety_check": True,
        "rollback_possible": True
    },
    {
        "description": "Process and validate data",
        "action": "process_data",
        "parameters": {"validation_rules": ["email", "age"]},
        "expected_output": "Validated user data",
        "safety_check": True,
        "rollback_possible": True
    }
]

# Create a plan
plan = planner.create_plan(...

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